Once
again, we’re focusing on how IT leaders are improving their business
performance for better access, use and analysis of their data and
information. This time we’re coming to you directly from the recent HP Vertica Big Data Conference in Boston.

So please join me now in welcoming Craig Snodgrass, Senior Vice President for Analytics and Product at Cardlytics Inc., based in Atlanta. Welcome, Craig. [Disclosure: HP is a sponsor of BriefingsDirect podcasts.]

Craig Snodgrass: Thanks for having me.

Gardner: At some point, you must have had a data infrastructure or legacy
setup that wasn't meeting your requirements. Tell us a little bit about
the journey that you've been on gaining better analytic results for
your business.

Snodgrass: As with any other company, our data was growing and growing and
growing. Also growing at the same time was the number of advertisers
that we were working with. Since our advertisers spanned multiple
categories -- they range from automotive, to retail, to restaurants, to
quick-serve -- the types of questions they were asking were different.

So
we had this intersection of more data and different questions happening
at a vertical level. Using our existing platform, we just couldn't
answer those questions in a timely manner, and we couldn't iterate around
being able to give our advertisers even more insights, because it was
just taking too long.

First, we weren’t able to even get
answers. Then, when there was the back-and-forth of wanting to
understand more or get more insight it just ended up taking
longer-and-longer. So at the end of the day, it came down to multiple
and unstructured questions, and we just couldn't get our old systems to respond fast
enough.

Gardner: Tell us a bit about Cardlytics. Who are your customers, and what do you do for them?

Growing the business

Snodgrass:
Our customers are essentially anybody who wants to grow their business.
That's probably a common answer, but they are advertisers. They're
folks who are used to traditional media, where when they do a TV or
radio ad. They're hitting everybody, people that were going to come to
their store anyways and people who probably weren’t going to come to
their store.

We're
able to target who they want to bring into their store through looking
at both debit-card and credit-card purchase data, all in an anonymized
manner. We’re able to look at past spending behavior, and say, based on
those spending behaviors, that these are the types of customers that are
most likely to come to your store and more importantly, most likely to
be a long-term customer for you.

We can target those,
we can deliver the advertising in the form of a reward, meaning the
customer actually gets something for the advertising experience. We
deliver that through their bank.

The bank is able to
do this for their customers as well. The reward comes from the bank, and
the advertiser gets a new channel to go bring in business. Then, we can
track for them over time what their return on ad-spend is. That’s not
an advantage they’ve had before with the traditional advertising they’ve
been doing.

It
works inside of retail, just as well as restaurants, subscriptions, and
the other categories that are out there as well.

Gardner: So it sounds like a win,
win, win. As a consumer, I'm going to get offers that are something more
than a blanket. It's going to be something targeted to me as the bank
that’s providing the credit card. They're going to get loyalty by having
a rewards effort that works. Then, of course, those people selling
goods and services have a new way of reaching and marketing those goods
and services in a way they can measure.

Snodgrass:
Yeah, and back to this idea of the multiple verticals. It
works inside of retail, just as well as restaurants, subscriptions, and
the other categories that are out there as well. So it's not just a
one-category type reward.

Gardner: But to make
it work, to make that value come across to the consumer, it needs to be a quality targeted effort. Therefore you need to take a lot of data and do a
lot of queries.

Snodgrass: You got it. A
customer will know quickly when something is not relevant. If you bring
in a customer for whom it may not be relevant or they weren’t the right
customer, they're not going to return.

The advertiser
isn't going to get their return on ad-spend. So it's actually in both
our interests to make sure we choose the right customers, because we
want to get that return on ad-spend for the advertisers as well.

Gardner: Craig, what sort of volume of data are we talking about here?

Intersecting growth

Snodgrass: We're doing roughly 10 terabytes
a year. From a volume standpoint, it's a combination of not just the
number of transactions we're bringing in, but the number of requests,
queries, and answers that we’re having to go against it. That
intersection of growth in volume and growth in questions is happening at
the same time.

For us right now, our data is
structured. I know a lot of companies are working on the unstructured
piece. We're in a world where in the payment systems and banking
systems, the data is relatively structured and that's what we get, which
is great. Our questions are unstructured. They're everywhere from
corporate real estate types of questions, to loyalty, to just random
questions that they've never known before.

One key
thing that we can do for advertisers is, at a minimum, answer two large
questions. What is my market share in an area? Typically, advertisers
only know when customers come into their store with that transaction.
They don't know where that customer goes and, obviously, they don't know
when people don’t come into their store.

We have that
full 360-degree view of what happens at the customer level, so we can
answer, for a geographic area or whatever area that an advertiser wants,
what is their market share and how is their market share trending
week-to-week.

The other piece is that when we do
targeting, there could be somebody that visits a location three times
over a certain time period. You don't know if they're somebody who shops
the category 30 times or if they only shop them three times. We can
actually answer share-of-wallet for a customer, and you can use that in
targeting, designing your campaigns, and more importantly, in analysis.
What's going on with these customers?

For us, with Vertica,
one of the key components isn't just the speed, but how quick we can
scale if the number of queries goes up.

Gardner:
So this is any marketers' dream. This is what people have been trying to
do and thinking about doing for decades, and now we’re able to get
there. One of the characteristics, though, if I understand your challenge
from a data-processing perspective, is not only the volume. You're
going to have many different queries hitting this at once, because
you have so many different verticals and customers. The better job you
do, the more queries will be generated.

Snodgrass: It's a self-fulfilling prophesy. For us, with Vertica,
one of the key components isn't just the speed, but how quick we can
scale if the number of queries goes up. It's relatively easy to predict
what our growth and data volume is going to be. It is not easy for me to
predict what the growth in queries is going to be. Again, as
advertisers understand what types of questions we can answer, it's
unfortunately a ratio of 10 to 1. Once they understand something, there
are 10 other questions that come out of it.

We can
quickly add nodes and scalability to manage the increase in volumes of
queries, and it's cheap. This is not expensive hardware that you have to
put in. That is one of the main decision points we had. Most people
understand HP Vertica on the speed piece, but that and the quick
scalability of the infrastructure were critical for us.

Gardner: Just as your marketing customers want to be able to predict their spend and the return on investment (ROI)
from it, do you sense that you can predict and appreciate, when you
scale with HP Vertica what your costs will be? Is there a big question mark
or do you have a sense of, I do this and I have to pay that?

Snodgrass:
It is the "I do this and I'll have to pay that," the linearness. For
those who understand Vertica, that’s a bit of a pun, but the linear
relationship is that if we need to scale, all we need to do is this.
It's very easy to forecast. I may not know the date for when I need to
add something, but I definitely know what the cost will be when we need
to add it.

Compare and contrast

Gardner:
How do you measure, in addition to that predictability of cost, your
benefits? Are there any speeds and feeds that you can share that compare
and contrast and might help us better understand how well this works?

Snodgrass: There are two numbers. During the POC
phase, we had a set of 10 to 15 different queries that we used as a
baseline. We saw anywhere from 500x to 1,000x or 1,500x speed in return
of getting that data. So that’s the first bullet point.

The
second is that there were queries that we just couldn't get to finish.
At some point, when you let it go long enough, you just don't know if it
is going to converge. With Vertica, we haven't hit that limit yet.

Vertica has also allowed to have varying degrees of analysts’ capabilities when it comes to SQL
writing. Some are elegant and they write fantastic, very efficient
queries. Others are still learning the best way to go put the queries
together. They will still always return with Vertica. In the legacy
world prior to Vertica, those are the ones that just wouldn't return.

In a SaaS shop, there are a lot of
things that you're going to do in SaaS that you are not going to go do
in SQL

I
don’t know the exact number for how much more productive they are, but
the fact that their queries are always returning, and returning in a
timely manner, obviously has dramatically increased their productivity.
So it's a hard one to measure, but forget how fast the queries have
returned, the productivity of our analyst has gone up dramatically.

Gardner:
What could an analytics platform do better for you? What would you like
to see coming down the pipeline in terms of features, function, and
performance?

Snodgrass: If you could do something in SQL, Vertica is fantastic. We'd like more integration with R, more integration with software as a service (SaaS),
more integration with these sophisticated tools. If you get all the
data into their systems, maybe they can manipulate it in a certain way,
but then, you are managing two systems.

Vertica is
working on a little bit better integration with R through distributed R,
but there's also SaaS as well. In a SaaS shop, there are a lot of
things that you're going to do in SaaS that you are not going to go do
in SQL. That next level of analytics integration is where we would love
to go see the product go.

Gardner: Last
question. Do you expect that there will be different types of data and
information that you could bring to bear on this? Perhaps
some sort of camera, sensor of some sort, point-of-sale
information, or mobile and geospatial information that could be brought
to bear? How important is it for you to have a platform that can
accommodate seemingly almost any number of different information types
and formats?

Snodgrass: The best way to answer
that one is that we don't ever want to tell business development that
the reason they can't pursue a path is because we don't have a platform
that can support that.

Different paths

Today,
I don't know where the future holds from these different paths, but
there are so many different paths we can go down. It's not just the
Vertica component, but the HP HAVEn
components and the fact that they can integrate with a lot of the
unstructured, I think they call it “the human data versus the machine
data.”

It's having the human data pathway open to us.
We don't want to be the limiting factor for why somebody would want to
do something. That's another bullet point for HP Vertica in our camp. If a
business model comes out, we can support it.

Gardner: Clearly there's a revolution taking place in retail, and it sounds like you are on the vanguard of that.

I don't know where the future holds from these different paths, but
there are so many different paths we can go down.

Snodgrass: Yeah, I agree.

Gardner:
Okay, well I'm afraid we'll have to leave it there. We've been learning
how data-intensive credit card marketing services provider Cardlytics
is providing millions of highly tailored marketing offers to their
banking consumers and customers for marketing activities in sales across
the U.S.

And we've also heard how they deployed an HP
Vertica Analytics Platform to provide better analytics to deliver those
insights to these many customers. So a big thank you to
our guest, Craig Snodgrass, Senior Vice President for Analytics and
Product at Cardlytics.

Snodgrass: Thank you, Dana.

Gardner:
And thank you also to our audience for joining us for this special HP
Discover Podcast coming to you directly from the recent HP Vertica
Big Data Conference in Boston.

I'm Dana Gardner,
Principal Analyst at Interarbor Solutions, your host for this ongoing
series of HP sponsored discussions. Thanks again for joining, and come
back next time.

Transcript
of a BriefingsDirect podcast on how a marketing company uses HP
Vertica to match advertisers with potential customers across an ever-growing expanse of data and queries. Copyright Interarbor Solutions, LLC, 2005-2013. All
rights reserved.